The World Wide Web is a distributed database comprising billions of data records accessible through the Internet. Search engines are commonly used to search the information available on computer networks, such as the World Wide Web, to enable users to locate data records of interest. A typical prior art search engine 100 is shown in FIG. 1. Web pages, hypertext documents, and other data records from a source 101, accessible via the Internet or other network, are collected by a crawler 102. Crawler 102 collects data records from source 101, using various methods and algorithms. For example, crawler 102 may follow hyperlinks in a collected hypertext document to collect other data records. The data records retrieved by crawler 102 are stored in a database 108. Thereafter, these data records are indexed by an indexer 104. Indexer 104 builds a searchable index of the documents in database 108. Common prior art methods for indexing may include inverted files, vector spaces, suffix structures, and hybrids thereof. For example, each web page may be broken down into words and respective locations of each word on the page. The pages are then indexed by the words and their respective locations. A primary index of the whole database 108 is then broken down into a plurality of sub-indices and each sub-index is sent to a search node in a search node cluster 106.
To use search engine 100, a user 112 typically enters one or more search terms or keywords, which are sent to a dispatcher 110. Dispatcher 110 compiles a list of search nodes in cluster 106 to execute the query and forwards the query to those selected search nodes. The search nodes in search node cluster 106 search respective parts of the primary index produced by indexer 104 and return sorted search results along with a document identifier and a score to dispatcher 110. Dispatcher 110 merges the received results to produce a final result set displayed to user 112 sorted by relevance scores. The relevance score is a function of the query itself and the type of document produced. Factors that affect the relevance score may include: a static relevance score for the document such as link cardinality and page quality, placement of the search terms in the document, such as titles, metadata, and document web address, document rank, such as a number of external data records referring to the document and the “level” of the data records, and document statistics such as query term frequency in the document, global term frequency, and term distances within the document. For example, Term Frequency Inverse Document Frequency (TFIDF) is a statistical technique that is suitable for evaluating how important a word is to a document. The importance increases proportionally to the number of times a word appears in the document but is offset by how common the word is in all of the documents in the collection.
Referring to FIG. 2, there is shown an example of a result set 120. As shown in the figure, in response to a query 122 for the search term “MP3 player” shown on the top of the figure, the search engine YAHOO! searched its web index and produced a plurality of results in the form of result set 120 displayed to a user. For brevity, only a first page of result set 120 is shown. Result set 120 includes ten results 124a-f, each with a respective clickable hyperlink 126a-j, description 127a-j, and Internet addresses or uniform resource locator (URL) 128a-j for data records that satisfy query 122.
In addition to displaying search results sorted by a relevance score, a search engine may display sponsored results 124a-c and 124g-j, which are pay-for-placement listings paid for by web page operators such as advertisers. An advertiser agrees to pay an amount of money to the search engine operator, commonly referred to as the bid amount, in exchange for a particular position in a set of search results that is generated in response to a user's input of a particular search term. A higher bid amount will result in a more prominent placement of the advertiser's website in a set of sponsored search results. Advertisers adjust their bids or bid amounts to control the position at which their search listings are presented in the sponsored search results. The pay-for-placement system places search listings having higher-value bids higher or closer to the top of the search listings. Higher-value bids may also be placed on a side bar, for example, as results 124g-j in FIG. 2. More prominent listings are seen by more users and are more likely to be clicked through, producing traffic of potential customers to the advertiser's web site.
Focusing on sponsored result 124a, each sponsor listing may include a clickable hyperlink title 126a, including anchor text “MP3 CD Walkman®,” descriptive text 127a, and a uniform resource locator (URL), www.sonystyle.com, 128a. Search engine 100 may store such sponsor listings, each associated with an advertiser or a web page operator, in database 108.
Search engine operators have developed various tools suitable for use in pay-for-placement systems to help the advertisers manage their bids and attract traffic. Referring to FIG. 3, there is shown an exemplary bidding tool 300. By way of example only, bidding tool 300 may include keywords 302, categories 304, monthly (or any other time period) search volumes 306 for each search term, maximum bids 308, positions 310, top 5 max bids 312, estimated monthly clicks 314, estimated monthly cost per click 316, and estimated monthly cost 318.
Keyword 302 is a search term, such as a word or a phrase, that relates to advertiser's business and describes its products or services. Category 304 defines a grouping of keywords that are similar in a particular way (e.g., product type). Monthly search volume 306 is a statistic indicating a number of monthly searches the advertiser can expect on a particular keyword based on historical data. Maximum bids 308 determine the maximum price the advertiser is willing to pay per click for a particular keyword. Positions 310 indicate the current position of the advertiser's listing in the search results based on the advertiser's max bid amount. Top 5 max bids 312 indicate the bid amounts for the top five bids on a particular keyword. Estimated monthly clicks 314 indicate the estimated number of total clicks the advertiser will receive from a keyword based on the advertiser's max bid. Estimated monthly CPC 316 indicates the advertiser's estimated average cost-per-click on a particular keyword based on the advertiser's max bid. Estimated monthly cost 318 indicates the advertiser's estimated total monthly cost on a particular keyword based on the estimated monthly clicks and estimated CPC.
Focusing on the search term “mp3 players” 302a, which belongs to category mp3 304a, an advertiser using a bidding tool 300 may observe that search term 302a has been searched for by Yahoo! users approximately 540,000 times in the preceding month, as indicated by the corresponding monthly search volume 306a. As further indicated by top five max bids 312a, top five maximum bid for search term 302a range from $0.53 to $2.00 per click. As further indicated by maximum bid 308, the advertiser must bid at least $2.01 for search term 302a to secure the most prominent placement of the advertiser's web site, among the sponsored search results. As further indicated by estimated clicks 314a, the most prominent placement position for search term 302a, may lead to approximately 17,714 clicks-through per month, with an associated monthly cost 318a for the advertiser of $35,605.14.
Thus, when a user performs a search on a pay-for-placement search engine, the sponsored results are conventionally sorted and displayed based on how much each advertiser has bid on the user's search term. Because different users will use different keywords to find the same information, it is important for an advertiser to bid on a wide variety of search terms in order to maximize the traffic to the advertiser's website. Thus, advertisers may attempt to place high bids on more than one search term to increase the likelihood that their websites will be seen as a result of a search for those terms. For example, the advertiser may decide to place bids on all search terms 302a-e shown in FIG. 3. The better and more extensive an advertiser's list of search terms, the more traffic the advertiser will see. However, there are many similar search terms for which the advertiser many not have bid. As a result, the advertiser can miss opportunities for advertising placement when these similar search terms are used, and the search engine operator may not receive any revenue from searches performed using such search terms for which there have been no bids.
Even in the context of non-sponsored searches, or search results that do not involve pay-for-placement listings, a search engine user is disadvantaged by the lack of intelligent searching of search terms that are similar to those typed into the search engine. This is because the search will produce limited results that do not necessary reflect the user's intent in searching. In some systems, there is some spell-checking that is performed on key words that are typed into the search engine. However, word searches on similar terms, or suggested searches using similar terms with respect to the typed keywords, are not provided in these systems.
Accordingly, there is a need for a system and method that would provide searches or suggested searches of search terms that are similar or related to search terms typed in by a search engine user.
There is also a need for a system and method for searching unbidded search terms in a sponsored search system that are similar or related to those typed in by a user.